For zero level-set samples \(\mathbf {x}_z\) , one intuitive solution is to construct voxel grids and estimate the SDF value for each vertex. The zero level-set samples can then be easily obtained on the extracted triangle mesh. However, due to incomplete observations, spurious zero level-set samples may be generated in unseen areas. Additional maintenance of occupancy status for each voxel grid is required to eliminate erroneous samples, which is memory inefficient. Here, we take a simple solution to randomly down-sample previous observations and maintain a fixed size of buffer data [1]} to regularize the data term \(|f(\mathbf {x}_z;\theta )|\) . Experimental results show that this simple solution is effective enough to store previous knowledge without catastrophic forgetting.
